IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v244y2016i1d10.1007_s10479-016-2178-4.html
   My bibliography  Save this article

A novel reliability allocation approach using the OWA tree and soft set

Author

Listed:
  • Kuei-Hu Chang

    (R.O.C. Military Academy)

Abstract

Reliability allocation is a crucial task in product design and development, directly influencing the quality and market competitiveness of a product. Traditional techniques for allocation of product reliability include the equalized allocation, ARINC, and AGREE allocation methods. Although these methods are simple and widely used in industry, they do not perform simultaneous allocation of the reliability of root and leaf nodes. Moreover, situations arise in the initial stages of product design in which there is incomplete information, increasing the difficulty of reliability allocation, can not be solved effectively using traditional reliability allocation methods. To resolve these issues, this paper integrates the ordered weighted averaging tree and soft set approach with regard to flexible allocation product reliability. To verify the proposed approach, a numerical example of a thin-film transistor liquid crystal display product is adopted. This paper also compares the results of the simulation with those of the equalized allocation, ARINC allocation, and AGREE allocation methods in dealing with incomplete information. The results demonstrate that the proposed method is more flexible and does not lose any valuable information that is provided by experts.

Suggested Citation

  • Kuei-Hu Chang, 2016. "A novel reliability allocation approach using the OWA tree and soft set," Annals of Operations Research, Springer, vol. 244(1), pages 3-22, September.
    • Handle: RePEc:spr:annopr:v:244:y:2016:i:1:d:10.1007_s10479-016-2178-4
    DOI: 10.1007/s10479-016-2178-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-016-2178-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-016-2178-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kim, Kyungmee O. & Yang, Yoonjung & Zuo, Ming J., 2013. "A new reliability allocation weight for reducing the occurrence of severe failure effects," Reliability Engineering and System Safety, Elsevier, vol. 117(C), pages 81-88.
    2. B. Ahn & S. Choi, 2012. "Aggregation of ordinal data using ordered weighted averaging operator weights," Annals of Operations Research, Springer, vol. 201(1), pages 1-16, December.
    3. Duan Li & Xiaoling Sun & Ken McKinnon, 2005. "An Exact Solution Method for Reliability Optimization in Complex Systems," Annals of Operations Research, Springer, vol. 133(1), pages 129-148, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kuei-Hu Chang, 2019. "A novel supplier selection method that integrates the intuitionistic fuzzy weighted averaging method and a soft set with imprecise data," Annals of Operations Research, Springer, vol. 272(1), pages 139-157, January.
    2. Hong-Bin Yan & Tieju Ma & Songsak Sriboonchitta & Van-Nam Huynh, 2017. "A stochastic dominance based approach to consumer-oriented Kansei evaluation with multiple priorities," Annals of Operations Research, Springer, vol. 256(2), pages 329-357, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hamideh Jeddi & Mahdi Doostparast, 2022. "Allocation of redundancies in systems: a general dependency-base framework," Annals of Operations Research, Springer, vol. 312(1), pages 259-273, May.
    2. Hsu-Shih Shih, 2016. "A Mixed-Data Evaluation in Group TOPSIS with Differentiated Decision Power," Group Decision and Negotiation, Springer, vol. 25(3), pages 537-565, May.
    3. Yixiong Feng & Zhaoxi Hong & Jin Cheng & Likai Jia & Jianrong Tan, 2017. "Low Carbon-Oriented Optimal Reliability Design with Interval Product Failure Analysis and Grey Correlation Analysis," Sustainability, MDPI, vol. 9(3), pages 1-14, March.
    4. João Carneiro & Luís Conceição & Diogo Martinho & Goreti Marreiros & Paulo Novais, 2018. "Including cognitive aspects in multiple criteria decision analysis," Annals of Operations Research, Springer, vol. 265(2), pages 269-291, June.
    5. Yadav, Om Prakash & Zhuang, Xing, 2014. "A practical reliability allocation method considering modified criticality factors," Reliability Engineering and System Safety, Elsevier, vol. 129(C), pages 57-65.
    6. Remigiusz Gawlik & Dominika Siwiec & Andrzej Pacana, 2024. "Quality–Cost–Environment Assessment of Sustainable Manufacturing of Photovoltaic Panels," Energies, MDPI, vol. 17(7), pages 1-17, March.
    7. Junzhe Bao & Xudong Li & Chuanhua Yu, 2015. "The Construction and Validation of the Heat Vulnerability Index, a Review," IJERPH, MDPI, vol. 12(7), pages 1-15, June.
    8. Ding, Rui & Liu, Zehua & Xu, Jintao & Meng, Fanpeng & Sui, Yang & Men, Xinhong, 2021. "A novel approach for reliability assessment of residual heat removal system for HPR1000 based on failure mode and effect analysis, fault tree analysis, and fuzzy Bayesian network methods," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    9. Sébastien Destercke, 2017. "On the median in imprecise ordinal problems," Annals of Operations Research, Springer, vol. 256(2), pages 375-392, September.
    10. Carayannis, Elias G. & Goletsis, Yorgos & Grigoroudis, Evangelos, 2018. "Composite innovation metrics: MCDA and the Quadruple Innovation Helix framework," Technological Forecasting and Social Change, Elsevier, vol. 131(C), pages 4-17.
    11. Roger Chapman Burk & Richard M. Nehring, 2023. "An Empirical Comparison of Rank-Based Surrogate Weights in Additive Multiattribute Decision Analysis," Decision Analysis, INFORMS, vol. 20(1), pages 55-72, March.
    12. Ivona Ivić & Anita Cerić, 2024. "Mitigation Measures for Information Asymmetry between Participants in Construction Projects: The Impact of Trust," Sustainability, MDPI, vol. 16(16), pages 1-27, August.
    13. de Almeida Filho, Adiel T. & Clemente, Thárcylla R.N. & Morais, Danielle Costa & de Almeida, Adiel Teixeira, 2018. "Preference modeling experiments with surrogate weighting procedures for the PROMETHEE method," European Journal of Operational Research, Elsevier, vol. 264(2), pages 453-461.
    14. Wenjun Chang & Chao Fu & Nanping Feng & Shanlin Yang, 2021. "Multi-criteria Group Decision Making with Various Ordinal Assessments," Group Decision and Negotiation, Springer, vol. 30(6), pages 1285-1314, December.
    15. Rick A. Jones & Barbara A. Luckett & Peter A. Beling & Barry M. Horowitz, 2013. "Architectural scoring framework for the creation and evaluation of System-Aware Cyber Security solutions," Environment Systems and Decisions, Springer, vol. 33(3), pages 341-361, September.
    16. Kim, Kyungmee O. & Zuo, Ming J., 2018. "Optimal allocation of reliability improvement target based on the failure risk and improvement cost," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 104-110.
    17. Reimann, Olivier & Schumacher, Christian & Vetschera, Rudolf, 2017. "How well does the OWA operator represent real preferences?," European Journal of Operational Research, Elsevier, vol. 258(3), pages 993-1003.
    18. Chao Fu & Dong-Ling Xu, 2016. "Determining attribute weights to improve solution reliability and its application to selecting leading industries," Annals of Operations Research, Springer, vol. 245(1), pages 401-426, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:annopr:v:244:y:2016:i:1:d:10.1007_s10479-016-2178-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.